Abstract
The object recognition model described in this paper enhances the performance of recent pioneering attempts that simulate the primary visual cortex operations. Images are transformed into the log-polar space in order to achieve rotation invariance, resembling the receptive fields (RF) of retinal cells. Via the L*a*b colour-opponent space and log-Gabor filters, colour and shape features are processed in a manner similar to V1 cortical cells. Visual attention is employed to isolate an object’s regions of interest (ROI) and through hierarchicallayers visual information is reduced to vector sequences learned by a classifier. Template matching is performed with the normalised cross-correlation coefficient and results are obtained from the frequently used Support Vector Machine (SVM) and a Spectral Regression Discriminant Analysis (SRDA) classifier. Experiments on five different datasets demonstrate that the proposed model has an improved recognition rate and robust rotation invariance with low standard deviation values across the rotation angles examined.
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References
Mishkin, M., Ungerleider, G., Macko, A.K.: Object vision and spatial vision: Two cortical pathways. Trends in Neuroscience 6, 414–417 (1983)
Han, S., Vasconcelos, N.: Biologically plausible saliency mechanisms improve feedforward object recognition. Vision Research 50, 2295–2307 (2010)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)
Tsitiridis, A., Yuen, P., Hong, K., Chen, T., Kam, F., Jackman, J., James, D., Richardson, M.: A biological cortex-like target recognition and tracking in cluttered background. In: SPIE, Optics and Photonics for Counterterrorism and Crime Fighting, Berlin, p. 74860G (2009)
Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: Advances in Neural Information Processing Systems, vol. 19 (2007)
Mutch, J., Lowe, D.: Object class recognition and localisation using sparse features with limited receptive fields. International Journal of Computer Vision 80, 45–57 (2008)
Elazary, L., Itti, L.: A Bayesian model for efficient visual search and recognition. Vision Research 50, 1338–1352 (2010)
Lowe, D.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M.: Robust Object Recognition with Cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 411–426 (2007)
Tsitiridis, A., Richardson, M., Yuen, P.: Salient feature-based object recogniton in cortex-like machine vision. Engineerng Intelligent Systems. Special Is. (2012)
Engel, S., Zhang, X., Wandell, B.: Colour Tuning in Human Visual Cortex Measured with functional Magnetic Resonance Imaging. Nature 388, 68–71 (1997)
Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of Optical Society of America 2, 1160–1169 (1985)
Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. Journal of Optical Society of America A 4, 2379–2394 (1987)
DeValois, R., Albrecht, D., Thorell, L.: Spatial Frequency Selectivity of Cells in Macaque Visual Cortex. Vision Research 22, 545–559 (1982)
Fischer, S., Sroubek, F., Perrinet, L., Redondo, R., Cristobal, G.: Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision 75, 231–246 (2007)
Van Essen, D.C., Newsome, T.W., Maunsell, J.H.: The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies and individual variability. Vision Research 24, 429–448 (1984)
Johnston, A.: A spatial property of the retino-cortical mapping. Spatial Vision 1, 319–331 (1986)
Hollard, V.D., Delius, J.D.: Rotational Invariance in Visual Pattern Recognition. Science 218, 804–806 (1982)
Harris, I.M., Dux, P.E.: Orientation-invariant object recognition: evidence from repetition blindness. Cognition 95, 73–93 (2005)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Machine Learning Research 9, 1871–1874 (2008)
Cai, D., He, X., Han, J.: SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis. IEEE Transactions on Knowledge and Data Engineering 20, 1–12 (2008)
Lazebnik, S., Schmid, C., Ponce, J.: Semi-Local Affine Parts for Object Recognition. In: Proceedings of the British Machine Vision Conference, London, pp. 959–968 (2004)
Lazebnik, S., Schmid, C., Ponce, J.: A Sparse Texture Representation Using Local Affine Regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1265–1278 (2005)
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200 (2010)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: Computer Vision and Pattern Recognition (2009)
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Tsitiridis, A., Mora, B., Richardson, M. (2013). Hierarchical Object Recognition Model of Increased Invariance. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_20
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DOI: https://doi.org/10.1007/978-3-642-41013-0_20
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